Data Engineering and Equity Analysis

ETL Process: Transforming Data from Multiple Sources for Analysis This process outlines an ETL (Extract, Transform, Load) pipeline that ingests data from two different sources, cleans and transforms it, and ultimately stores it in a centralized location for further analysis. Additionally, it introduces a new file named equity_analysis where the results from the initial ETL process are further analyzed for each company with detailed calculations and visualizations.

1. Extract:

• Data is retrieved from two sources:
  • API: Data is extracted from an API using a library like yfinance (for financial data) or a custom API client depending on the specific API.
  • CSV: A CSV file containing relevant data is loaded using pandas.

2. Transform:

• The extracted data undergoes various transformations to prepare it for analysis:
  • Cleaning: Data is inspected for missing values, inconsistencies, and errors. Cleaning might involve imputation, handling outliers, and correcting data formats.
  • Merging: Data from the API and CSV may be merged based on a common identifier (e.g., company ID) to combine relevant information.
  • Calculation of Financial Metrics:
    • Revenue Growth: This involves calculating the Compound Annual Growth Rate (CAGR) of the revenue data.
    • EPS Growth: Similar to revenue growth, CAGR is calculated for the earnings per share (EPS) data.
    • PE Ratio: The price-to-earnings ratio (PE) is computed by dividing the stock price by the EPS.
    • Future Price Estimates: Based on the calculated growth rates and current financial metrics, estimates for future revenue, EPS, and stock prices are generated. This might involve extrapolating trends or using more sophisticated financial modeling techniques.

Known Issues:

• Data Reliance: This process relies on the availability and accuracy of data provided by the Yahoo Finance API.
• Null Values: The PE ratio and potentially other calculations may return Null values if the EPS is not a positive number or there is no data available for a specific year (e.g., 2020) in the Yahoo Finance API. You may want to implement additional logic to handle these cases (e.g., excluding rows with Null values or using alternative data sources).

3. Load:

• The final, transformed data containing the above-mentioned financial metrics and estimates is loaded into a centralized storage location:
  • Storage Account: Azure Blob Storage is a common option, allowing efficient storage and retrieval of large datasets. Other cloud storage options like Amazon S3 or Google Cloud Storage can also be used.

Equity Analysis:

A new file named equity_analysis has been created to perform in-depth analysis for each company based on the transformed data from the initial ETL process. This analysis includes detailed calculations and visualizations to provide insights into individual companies' financial performance and potential investment opportunities.

Benefits:

• Centralized Data: This process ensures consistent and readily available data for analysis, eliminating the need to manage data from scattered sources.
• Improved Data Quality: Cleaning and transformation steps improve data quality, leading to more reliable and insightful analysis.
• Actionable Insights: The calculated financial metrics and future price estimates provide valuable insights for investment decisions, financial forecasting, or other data-driven applications.
• Scalability: The ETL process can be automated and scaled to handle larger datasets efficiently.

This ETL pipeline, along with the equity analysis, provides a structured approach to integrate and prepare data from diverse sources, transforming it into valuable financial metrics and insights for further analysis and decision-making.